Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 MiB
Average record size in memory168.0 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
long is highly overall correlated with zipcodeHigh correlation
price is highly overall correlated with grade and 3 other fieldsHigh correlation
sqft_above is highly overall correlated with bathrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_living15 is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
sqft_basement has 13126 (60.7%) zerosZeros
yr_renovated has 20699 (95.8%) zerosZeros

Reproduction

Analysis started2024-09-17 11:42:53.861360
Analysis finished2024-09-17 11:43:41.207084
Duration47.35 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct21436
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5803015 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:41.361588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1248034 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089004 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8765656 × 109
Coefficient of variation (CV)0.62802974
Kurtosis-1.2605419
Mean4.5803015 × 109
Median Absolute Deviation (MAD)2.4025301 × 109
Skewness0.24332855
Sum9.8994057 × 1013
Variance8.2746295 × 1018
MonotonicityNot monotonic
2024-09-17T14:43:41.590801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
8651510380 2
 
< 0.1%
2568300045 2
 
< 0.1%
9353300600 2
 
< 0.1%
4139480200 2
 
< 0.1%
1954420170 2
 
< 0.1%
6381500170 2
 
< 0.1%
7167000040 2
 
< 0.1%
9407110710 2
 
< 0.1%
1000102 2
 
< 0.1%
Other values (21426) 21592
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2024-09-17T14:43:41.809704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:41.984291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540088.14
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:42.165053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1156480
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367127.2
Coefficient of variation (CV)0.67975423
Kurtosis34.58554
Mean540088.14
Median Absolute Deviation (MAD)150000
Skewness4.0240691
Sum1.1672925 × 1010
Variance1.3478238 × 1011
MonotonicityNot monotonic
2024-09-17T14:43:42.372042image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 172
 
0.8%
450000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (4018) 20113
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708416
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:42.551757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93006183
Coefficient of variation (CV)0.27591383
Kurtosis49.063653
Mean3.3708416
Median Absolute Deviation (MAD)1
Skewness1.9742995
Sum72854
Variance0.86501501
MonotonicityNot monotonic
2024-09-17T14:43:42.711583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.8%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.8%
3 9824
45.5%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147573
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:42.923157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77016316
Coefficient of variation (CV)0.36418512
Kurtosis1.2799024
Mean2.1147573
Median Absolute Deviation (MAD)0.5
Skewness0.51110757
Sum45706.25
Variance0.59315129
MonotonicityNot monotonic
2024-09-17T14:43:43.100794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5380
24.9%
1 3852
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1446
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (20) 652
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 72
 
0.3%
1 3852
17.8%
1.25 9
 
< 0.1%
1.5 1446
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5380
24.9%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:43.348475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2024-09-17T14:43:43.559712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1028) 20318
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:43.825565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityNot monotonic
2024-09-17T14:43:44.043374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19818
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:44.219822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2024-09-17T14:43:44.369262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10680
49.4%
2 8241
38.1%
1.5 1910
 
8.8%
3 613
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10680
49.4%
1.5 1910
 
8.8%
2 8241
38.1%
2.5 161
 
0.7%
3 613
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 613
 
2.8%
2.5 161
 
0.7%
2 8241
38.1%
1.5 1910
 
8.8%
1 10680
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Length

2024-09-17T14:43:44.652534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T14:43:44.791968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Length

2024-09-17T14:43:44.930847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T14:43:45.087738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Length

2024-09-17T14:43:45.420896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-17T14:43:45.726887image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21613
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568732
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:46.050352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754588
Coefficient of variation (CV)0.15351681
Kurtosis1.1909321
Mean7.6568732
Median Absolute Deviation (MAD)1
Skewness0.7711032
Sum165488
Variance1.3817033
MonotonicityNot monotonic
2024-09-17T14:43:46.305109image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.2%
11 399
 
1.8%
5 242
 
1.1%
12 90
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
10 1134
 
5.2%
11 399
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 90
 
0.4%
11 399
 
1.8%
10 1134
 
5.2%
9 2615
 
12.1%
8 6068
28.1%
7 8981
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.3907
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:46.574708image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation828.09098
Coefficient of variation (CV)0.46303695
Kurtosis3.4023036
Mean1788.3907
Median Absolute Deviation (MAD)450
Skewness1.4466645
Sum38652488
Variance685734.67
MonotonicityNot monotonic
2024-09-17T14:43:46.892336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (936) 19724
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

ZEROS 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.50905
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:47.175759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.57504
Coefficient of variation (CV)1.5182206
Kurtosis2.7155742
Mean291.50905
Median Absolute Deviation (MAD)0
Skewness1.5779651
Sum6300385
Variance195872.67
MonotonicityNot monotonic
2024-09-17T14:43:47.498063image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13126
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6901
31.9%
ValueCountFrequency (%)
0 13126
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0051
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:47.908572image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.373411
Coefficient of variation (CV)0.014902757
Kurtosis-0.6574075
Mean1971.0051
Median Absolute Deviation (MAD)23
Skewness-0.4698054
Sum42599334
Variance862.79726
MonotonicityNot monotonic
2024-09-17T14:43:48.356097image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 422
 
2.0%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17326
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.402258
Minimum0
Maximum2015
Zeros20699
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:48.674783image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation401.67924
Coefficient of variation (CV)4.7591054
Kurtosis18.701152
Mean84.402258
Median Absolute Deviation (MAD)0
Skewness4.5494934
Sum1824186
Variance161346.21
MonotonicityNot monotonic
2024-09-17T14:43:49.035400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20699
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 25
 
0.1%
2006 24
 
0.1%
Other values (60) 570
 
2.6%
ValueCountFrequency (%)
0 20699
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.94
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:49.346146image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.505026
Coefficient of variation (CV)0.00054553579
Kurtosis-0.85347887
Mean98077.94
Median Absolute Deviation (MAD)42
Skewness0.40566121
Sum2.1197585 × 109
Variance2862.7878
MonotonicityNot monotonic
2024-09-17T14:43:49.765216image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 590
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 548
 
2.5%
98034 545
 
2.5%
98118 508
 
2.4%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16113
74.6%
ValueCountFrequency (%)
98001 362
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560053
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:50.196004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.13856371
Coefficient of variation (CV)0.0029134474
Kurtosis-0.676313
Mean47.560053
Median Absolute Deviation (MAD)0.1049
Skewness-0.48527048
Sum1027915.4
Variance0.019199902
MonotonicityNot monotonic
2024-09-17T14:43:50.679974image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.6624 17
 
0.1%
47.5322 17
 
0.1%
47.6846 17
 
0.1%
47.5491 17
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.6711 16
 
0.1%
47.5402 15
 
0.1%
47.6842 15
 
0.1%
47.6904 15
 
0.1%
Other values (5024) 21452
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size169.0 KiB
2024-09-17T14:43:51.334354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14082834
Coefficient of variation (CV)-0.0011523104
Kurtosis1.0495009
Mean-122.2139
Median Absolute Deviation (MAD)0.101
Skewness0.88505298
Sum-2641408.9
Variance0.019832622
MonotonicityNot monotonic
2024-09-17T14:43:51.743691image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 116
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20601
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5525
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:52.175643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913
Coefficient of variation (CV)0.34501545
Kurtosis1.5970958
Mean1986.5525
Median Absolute Deviation (MAD)410
Skewness1.1081813
Sum42935359
Variance469761.24
MonotonicityNot monotonic
2024-09-17T14:43:52.632882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 181
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 165
 
0.8%
Other values (767) 19849
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.456
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-09-17T14:43:53.052619image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.18
Coefficient of variation (CV)2.1384089
Kurtosis150.76311
Mean12768.456
Median Absolute Deviation (MAD)2505
Skewness9.5067432
Sum2.7596463 × 108
Variance7.4551823 × 108
MonotonicityNot monotonic
2024-09-17T14:43:53.408452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 357
 
1.7%
6000 289
 
1.3%
7200 211
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8679) 19595
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2024-09-17T14:43:37.640632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:55.412904image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:57.998751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:00.438699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:02.854718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:06.350019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:09.751289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:12.816613image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:15.031700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:17.124982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:19.496549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.675751image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:24.062939image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
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2024-09-17T14:43:16.417227image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:18.730606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.098189image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:23.354101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:26.464355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:29.194311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:31.862409image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:34.412608image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:36.991071image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:39.378435image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:57.421074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:59.914294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:02.204731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:05.601838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:08.980153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:12.236614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:14.544996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:16.541013image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:18.957406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.222958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:23.490072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:26.614313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:29.319269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:32.095397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:34.584605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:37.131656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:39.559178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:57.582966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:00.082505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:02.376898image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:05.847313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:09.167418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:12.411114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:14.683761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:16.667545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:19.094129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.336700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:23.596965image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:26.742412image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:29.444673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:32.268820image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:34.946710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:37.262426image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:39.727130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:57.750077image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:00.207538image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:02.539937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:06.012962image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:09.353235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:12.563004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:14.806344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:16.924809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:19.226593image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.443291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:23.737031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:26.879342image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:29.573104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:32.412920image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:35.061379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:37.391278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:39.899051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:42:57.876151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:00.331184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:02.712733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:06.175157image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:09.556737image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:12.689757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:14.929605image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:17.034130image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:19.361218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:21.568646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:23.904375image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:27.001666image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:29.699132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:32.544829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:35.210328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-09-17T14:43:37.527159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-09-17T14:43:53.737928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5210.1300.5470.6580.0150.0080.2620.4970.6910.1920.7460.5700.0690.0630.1140.1020.5670.043-0.205
bedrooms0.5211.0000.0240.2280.3810.006-0.0210.1910.3450.5400.2300.6470.4440.2170.2020.0380.0000.1800.017-0.167
condition0.1300.0241.0000.1790.1540.0300.0580.0810.0230.1070.0940.0600.0620.0390.0130.0250.0170.2480.0670.074
floors0.5470.2280.1791.0000.5020.0190.0250.1490.3220.599-0.2720.4010.305-0.234-0.2310.0240.0220.5520.013-0.061
grade0.6580.3810.1540.5021.0000.0200.1040.2230.6580.7120.0930.7160.6630.1520.1560.1430.1180.5010.016-0.182
id0.0150.0060.0300.0190.0201.000-0.0040.0070.0040.0040.0010.002-0.000-0.117-0.1150.0290.0060.027-0.017-0.005
lat0.008-0.0210.0580.0250.104-0.0041.000-0.1430.456-0.0260.1160.0310.028-0.122-0.1170.0680.034-0.1260.0250.250
long0.2620.1910.0810.1490.2230.007-0.1431.0000.0640.385-0.2000.2850.3800.3710.3730.0850.0960.413-0.075-0.577
price0.4970.3450.0230.3220.6580.0040.4560.0641.0000.5420.2520.6440.5720.0750.0630.2080.3200.1020.102-0.009
sqft_above0.6910.5400.1070.5990.7120.004-0.0260.3850.5421.000-0.1660.8440.6970.2720.2540.0890.0830.4720.031-0.279
sqft_basement0.1920.2300.094-0.2720.0930.0010.116-0.2000.252-0.1661.0000.3280.1300.0370.0310.1590.134-0.1780.0630.115
sqft_living0.7460.6470.0600.4010.7160.0020.0310.2850.6440.8440.3281.0000.7470.3040.2840.1490.1400.3520.053-0.207
sqft_living150.5700.4440.0620.3050.663-0.0000.0280.3800.5720.6970.1300.7471.0000.3600.3660.1470.0890.336-0.006-0.287
sqft_lot0.0690.2170.039-0.2340.152-0.117-0.1220.3710.0750.2720.0370.3040.3601.0000.9220.0400.014-0.0380.009-0.319
sqft_lot150.0630.2020.013-0.2310.156-0.115-0.1170.3730.0630.2540.0310.2840.3660.9221.0000.0350.000-0.0160.009-0.326
view0.1140.0380.0250.0240.1430.0290.0680.0850.2080.0890.1590.1490.1470.0400.0351.0000.5920.0410.1090.074
waterfront0.1020.0000.0170.0220.1180.0060.0340.0960.3200.0830.1340.1400.0890.0140.0000.5921.0000.0320.0920.079
yr_built0.5670.1800.2480.5520.5010.027-0.1260.4130.1020.472-0.1780.3520.336-0.038-0.0160.0410.0321.000-0.215-0.317
yr_renovated0.0430.0170.0670.0130.016-0.0170.025-0.0750.1020.0310.0630.053-0.0060.0090.0090.1090.092-0.2151.0000.062
zipcode-0.205-0.1670.074-0.061-0.182-0.0050.250-0.577-0.009-0.2790.115-0.207-0.287-0.319-0.3260.0740.079-0.3170.0621.000

Missing values

2024-09-17T14:43:40.208319image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-17T14:43:40.908251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
0712930052020141013T000000221900.031.00118056501.0003711800195509817847.5112-122.25713405650
1641410019220141209T000000538000.032.25257072422.000372170400195119919812547.7210-122.31916907639
2563150040020150225T000000180000.021.00770100001.000367700193309802847.7379-122.23327208062
3248720087520141209T000000604000.043.00196050001.000571050910196509813647.5208-122.39313605000
4195440051020150218T000000510000.032.00168080801.0003816800198709807447.6168-122.04518007503
5723755031020140512T0000001225000.044.5054201019301.00031138901530200109805347.6561-122.0054760101930
6132140006020140627T000000257500.032.25171568192.0003717150199509800347.3097-122.32722386819
7200800027020150115T000000291850.031.50106097111.0003710600196309819847.4095-122.31516509711
8241460012620150415T000000229500.031.00178074701.000371050730196009814647.5123-122.33717808113
9379350016020150312T000000323000.032.50189065602.0003718900200309803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
21603785214004020140825T000000507250.032.50227055362.0003822700200309806547.5389-121.88122705731
21604983420136720150126T000000429000.032.00149011263.0003814900201409814447.5699-122.28814001230
21605344890021020141014T000000610685.042.50252060232.0003925200201409805647.5137-122.16725206023
21606793600042920150326T0000001007500.043.50351072002.000392600910200909813647.5537-122.39820506200
21607299780002120150219T000000475000.032.50131012942.000381180130200809811647.5773-122.40913301265
2160826300001820140521T000000360000.032.50153011313.0003815300200909810347.6993-122.34615301509
21609660006012020150223T000000400000.042.50231058132.0003823100201409814647.5107-122.36218307200
21610152330014120140623T000000402101.020.75102013502.0003710200200909814447.5944-122.29910202007
2161129131010020150116T000000400000.032.50160023882.0003816000200409802747.5345-122.06914101287
21612152330015720141015T000000325000.020.75102010762.0003710200200809814447.5941-122.29910201357